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Learn By Example: Pandas

Complete Your Data Science Toolbox with this Essential Python Library

It's no secret that data scientists stand to make a pretty penny in today's data-driven world; but if you're keen on becoming one, you'll need to master the appropriate tools. Pandas is one of the most popular of the Python data science libraries for working with mounds of data. By expressing data in a tabular format, Pandas makes it easy to perform data cleaning, aggregations and other analyses. Built around hands-on demos, this course will walk you through using Pandas and what it can do as you take on series, data frames, importing/exporting data, and more.

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: NumPy

Start Working with Multidimensional Data as You Dive Into This Python Library

Today's companies collect and utilize a staggering amount of data to guide their business decisions. But, it needs to be properly cleaned and organized before it can be put to use. Enter NumPy, a core library in the Python data science stack used by data science gurus to wrangle vast amounts of multidimensional data. This course will take you through NumPy's basic operations, universal functions, and more as you learn from hands-on examples.

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Seaborn

Go From Mounds of Data to Detailed Insights Using This Powerful Visualization Tool

From tech to medicine and finance, data plays a pivotal role in guiding today's businesses. But, it needs to be properly broken down and visualized before you can get any sort of actionable insights. That's where Seaborn comes into play. Designed for enhanced data visualization, this Python-based library helps bridge the gap between vast swathes of data and the valuable insights they contain. This course acts as your Seaborne guide, walking you through what it can do and how you can use it to display information, find relationships, and much more.

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Matplotlib

Build Professional Graphs & Plots with this Essential Visualization Tool

Before a data scientist can properly analyze their data, they must first visualize it and understand any relationships that might exist in the information. To this end, many data professionals use Matplotlib, an industry-favorite Python library for visualizing data. Highly customizable and packed with powerful features for building graphs and plots, Matplotlib is an essential tool for any aspiring data scientist, and this course will show you how it ticks.

Access 30 lectures & 3 hours of content 24/7

Explore the anatomy of a Matplotlib figure & its customizable parts

Dive into figures, axes, subplots & more components

Learn how to draw statistical insights from data

Understand different ways of conveying statistical information

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Spark 2.x

Come to Grips with Spark & Pull Valuable Insights from Your Data

One of the most popular data analytics engines out there, Spark has become a staple in many a data scientist's toolbox; and the latest version, Spark 2.x, brings more efficient and intuitive features to the table. Jump into this comprehensive course, and you'll learn how to better analyze mounds of data, extract valuable insights, and more with Spark 2.x. Plus, this course comes loaded with hands-on examples to refine your knowledge, as you analyze data from restaurants listed on Zomato and churn through historical data from the Olympics and the FIFA world cup!

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Plotly

Create Insightful Charts & Graphs with Minimal Programming Know-How

You don't need to be a programming prodigy to get started in data science. Easy to use and highly accessible, Plotly is library in Python that lets you create complex plots and graphs with minimal programming know-how. From creating basic charts to adding motion to your visualizations, this course will walk you through the Plotly essentials with hands-on examples that you can follow.

Work w/ plots on your local machine or share them via the Plotly Cloud

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Spark Streaming 2.x

Handle Continuous Data Like a Pro as You Learn From Real-World Examples

In addition to handling vast amounts of batch data, Spark has extremely powerful support for continuous applications, or those with streaming data that is constantly updated and changes in real-time. Using the new and improved Spark 2.x, this course offers a deep dive into stream architectures and analyzing continuous data. You'll also follow along a number of real-world examples, like analyzing data from restaurants listed on Zomato and real-time Twitter data.

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: PyTorch

Explore & Create the Building Blocks That Power Today's AI with PyTorch

More companies are using the power of deep learning and neural networks to create advanced AI that learns on its own. From speech recognition software to recommendation systems, deep learning frameworks, like PyTorch, make creating these products easier. Jump in, and you'll get up to speed with PyTorch and its capabilities as you analyze a host of real-world datasets and build your own machine learning models.

Access 41 lectures & 3.5 hours of content 24/7

Understand neurons & neural networks and how they factor into machine learning

Explore the basic steps involved in training a neural network

Familiarize yourself w/ PyTorch & Python 3

Analyze air quality data, salary data & more real-world datasets

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Learn By Example: Apache MXNet

Fast, scalable, and packed with an intuitive API for machine learning, Apache MXNet is a deep learning framework that makes it easy to build machine learning applications that learn quickly and can run on a variety of devices. This course walks you through the Apache MXNet essentials so you can start creating your own neural networks, the building blocks that allow AI to learn on their own.

Access 31 lectures & 2 hours of content 24/7

Explore neurons & neural networks and how they factor into machine learning

Walk through the basic steps of training a neural network

Dive into building neural networks for classifying images & voices

Refine your training w/ real-world examples & datasets

Instructor

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertises at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

An Easy Introduction to Python

Become a Python Programmer in Just a Few Hours

Python is a general-purpose programming language which can be used to solve a wide variety of problems, be they in data analysis, machine learning, or web development. This course lays a foundation to start using Python, which considered one of the best first programming languages to learn. Even if you've never even thought about coding, this course will serve as your diving board to jump right in.

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

An Easy Introduction To Machine Learning Using Scikit-Learn

Dive Into Automated Decision-Making with Python's Scikit-Learn

Classification models play a key role in helping computers accurately predict outcomes, like when a banking program identifies loan applicants as low, medium, or high credit risks. This course offers an overview of machine learning with a focus on implementing classification models via Python's scikit-learn. If you're an aspiring developer or data scientist looking to take your machine learning knowledge further, this course is for you.

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertise at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

An Easy Introduction To AI And Deep Learning

Get Your Feet Wet with the Backbone to Siri, Self-Driving Cars & More

Deep learning isn't just about helping computers learn from data—it's about helping those machines determine what's important in those datasets. This is what allows for Tesla's Model S to drive on its own and for Siri to determine where the best brunch spots are. Using the machine learning workhorse that is TensorFlow, this course will show you how to build deep learning models and explore advanced AI capabilities with neural networks.

Access 62 lectures & 8.5 hours of content 24/7

Understand the anatomy of a TensorFlow program & basic constructs such as graphs, tensors, and constants

Loonycorn is comprised of a couple of individuals —Janani Ravi and Vitthal Srinivasan—who have honed their tech expertise at Google and Stanford. The team believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Important Details

Length of time users can access this course: lifetime

Access options: web

Certification of completion not included

Redemption deadline: redeem your code within 30 days of purchase

Experience level required: intermediate

Requirements

Internet required

Course Outline

You, This Course and Us

You, This Course and Us (2:38)

Source Code and PDFs

Datasets for all Labs

Installation

Install TensorFlow (6:24)

Install Jupyter Notebook (4:38)

Running on the GCP vs. Running on your local machine

Lab: Setting Up A GCP Account (6:59)

Lab: Using The Cloud Shell (6:01)

Datalab ~ Jupyter (3:00)

Lab: Creating And Working On A Datalab Instance (10:29)

Unsupervised Learning

Supervised and Unsupervised Learning (11:30)

Expressing Attributes as Numbers (5:33)

K-Means Clustering (15:14)

Lab: K-Means Clustering with 2-Dimensional Points in Space (8:51)

Lab: K-Means Clustering with Images (10:19)

Patterns in Data (3:19)

Principal Components Analysis (13:19)

Autoencoders (5:03)

Autoencoder Neural Network Architecture (9:04)

Lab: PCA on Stock Data - Matplotlib vs Autoencoders (14:15)

Stacked Autoencoders (4:27)

Lab: Stacked Autoencoder With Dropout (7:51)

Lab: Stacked Autoencoder With Regularization and He Initialization (6:14)